Visualization and Intelligent Systems Laboratory



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University of California, Riverside
900 University Avenue
Riverside, CA 92521-0425

Tel: (951)-827-3954

Bourns College of Engineering
NSF IGERT on Video Bioinformatics

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Keio University

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IEEE Biometrics Workshop 2014
IEEE Biometrics Workshop 2013
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Multibiometrics Book

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Last updated: July 1, 2017



Tracking in Uncalibrated Multiple Cameras, and View Invariant Representation and Recognition of Human Action

Presented by: Dr. Mubarak Shah

ABSTRACT: Automatically understanding human behavior from video sequences is a very challenging problem. This involves 'extraction' of relevant visual information from a video sequence, 'representation' of that information in a s uitable form, and 'interpretation' of visual information for the purpose of recognition and learning human behavior.

In this talk, first we will present our approach for tracking people in multiple cameras. We employ the novel appro ach of finding the limits of field of view (FOV) of a camera as visible in the other cameras. Using this informatio n, when a person is seen in one camera, we are able to predict all the other cameras in which this person will be vi sible. Moreover, we apply the FOV constraint to disambiguate between possible candidates for correspondence. Track ing in each individual camera needs to be resolved before such an analysis can be applied. We perform tracking in a single camera using background subtraction, followed by region correspondence. This takes into account the velocities, sizes and distance of bounding boxes obtained through connected component labeling.

In the second part of the talk, we will discuss automatically understanding human actions using motion trajectories derived from video sequences. Since an action takes place in 3-D, and is projected on 2-D image, depending on the v iewpoint of the camera the projected 2-D trajectory may vary. This may create a problem in interpretation of trajec tories at the higher level. However, if the representation of actions only captures characteristics, which are view -invariant, then the higher-level interpretation can proceed without any ambiguity. We will discuss a computational representation of human action to capture dramatic changes in a motion trajectory using spatio-temporal curvature o f 2-D trajectory. This representation is compact, view-invariant, and is capable of explaining an action in terms o f meaningful atomic units.